Motivated by the priorities highlighted by Texas Department of Transportation (TXDOT) and following the guidelines in the recent presidential “Executive Order on Maintaining American Leadership in Artificial Intelligence” in 2019, this proposal aims to utilize the state-of-the-art tools and techniques in the field of Artificial Intelligence and Data Science to automatically identify and report traffic-related anomalies and hazards using live traffic camera footage across major highways and arterial roads in the State of Texas.
The focus of this project is the investigation, testing and further development of a RFID system to efficiently maintain the existing transportation infrastructure; especially Roadways and Bridges. For Example, in the event of an active snowfall or blizzard, snow may pile up and ice over on the roadway and bridges causing hazardous conditions for cars and trucks. The current warning systems employed utilizes a LED display board that advises the drivers to take caution, but it does not provide data.
A road network, consisting of different, but interdependent, transportation infrastructure assets, such as pavements, bridges, signs, etc., supports the mobility, economy, and safety of our society as a whole. According to the 2017 American Society of Civil Engineers infrastructure report card, the U.S. highway system has been underfunded for years. In 2015, 21% of highway pavements are in poor condition, which costs motorists $120.5 billion in extra vehicle repairs and operating costs. Over all, there is a need of $836 billion in repairs and capital investment for America’s highway system.
This study addresses the question of how the PDRBs affect the timeliness of the recovery process of affected communities. This research will add to body of knowledge about post-disaster recovery by addressing gaps in the existing literature on integrated analysis of PDRBs.
The local governments (LGs) in the Unitized States are managing 3/4 of total 4 million miles of roadway and more than 1/2 of nearly 600,000 bridges, which are critical transportation infrastructure assets to support the mobility, economy, and homeland security in local communities and the nation as a whole. To maintain the aging transportation infrastructure in the state of good repair under the shrinking budget, the state Departments of Transportation (DOTs) have adopted asset management systems (AMSs) to conduct cost‐effective maintenance, rehabilitation, and reconstruction (MR&R).
I-80 is the first corridor in the Bay area to have Adaptive Ramp Metering capability. This CTEDD (Center for Transportation Equity Decisions and Dollars) supported research analyzes highway performance data from I-80 corridor along with user perception of the new capabilities to provide lessons to ITS (Intelligent Transportation Systems) planners, engineering practitioners, and policy-makers on future implementation of Adaptive ramp metering (ARM) in the Bay area.
The research objectives of this project are the maintenance of transportation infrastructure: i) to develop and improve the reference-free crack measurement (RACM) hardware system for field applications, ii) to develop a post-processing diagnostic framework, iii) to develop monitoring system based on internet of things. In the research category, the reference-free crack measurement system is an innovative technique to improve system efficiency and better maintenance of existing infrastructure.
Annually, transportation agencies spend several millions of dollars of expenditures for their rehabilitation works due to the problematic soils underneath the infrastructure assets. The soils in Dallas-Fort Worth region have high tendency to undergo swell-shrink behavior that contributes to the premature failure of pavements. The proposed research focused on conducting the laboratory tests on the field collected samples and validating their field performance using innovative data collection technologies.